REQUISITOS PARA LA EJECUCIÓN DE OBRAS

Spin Labeling

Thomas Lindner, Naomi Larsen, Olav Jansen, Michael Helle Magnetic Resonance Materials in Physics, Biology and Medicine (accepted) Abstract

Object

To accelerate super-selective Arterial Spin Labeling angiography by using a single con- trol condition denoted as cycled super-selective Arterial Spin Labeling.

Materials and Methods

A single non-selective control image is acquired that is shared by selective label images. Artery-selective imaging is possible by geometrically changing the position of the la- beling focus to more than one artery of interest during measurement. The presented approach is compared to conventional super-selective imaging in terms of its labeling efficiency inside and outside the labeling focus using numerical simulations and in-vivo measurements. Additionally, the signal-to-noise ratios of the images are compared to non-selective ASL angiography and analyzed using a two-way ANOVA test and calcu- lating the Pearsons correlation coefficients.

Results

The results indicate that the labeling efficiency is not reduced within the labeled artery, but can increase as a function of distance to the artery of interest when compared to

conventional super-selective ASL. In the final images, no statistically significant differ- ence of image quality can be observed while the acquisition duration could be reduced. Conclusion

Using super-selective Arterial Spin Labeling, a single non-selective control acquisition suffices for reconstructing selective angiograms of the cerebral vasculature, thereby ac- celerating image acquisition without notable loss of information.

Introduction

Non-contrast enhanced magnetic resonance angiography (NCE-MRA) methods are com- monly used for the detailed anatomical investigation of the intracranial arterial status and its function. Examples include time-of-flight (TOF) or phase-contrast angiography (PCA), but these are often limited as only the whole cerebral vasculature can be depicted at once [1–3]. Therefore, drawing conclusions about single arteries can be impeded, but would be important for instance in the evaluation of individual feeding arteries of tu- mors or arterio-venous malformations (AVM). Using MRI, the application of specialized approaches, such as selective Arterial Spin Labeling (ASL) methods allow for the vi- sualization of individual arteries, e.g. the major brain feeding vessels and even small intracranial branches [4–7]. Among different tagging approaches, pseudo-continuous ASL (pCASL) is often used for angiography [5, 8–10]. Furthermore, certain modifi- cations of pCASL make it possible to create a labeling spot rather than a plane that allows for selectively labeling a single artery (super-selective ASL) [7]. This approach was already successfully applied for selective angiography measurements in volunteers and patients [8, 9]. Like most ASL techniques, pCASL relies on the acquisition of two images (label and control), which are subsequently subtracted so that only the difference signal of the arteries is visible in the final images. Investigations of the spin behavior in super-selective ASL revealed oscillations of the labeling efficiency outside the artery of interest [7]. Therefore, the label and control images should be acquired using the same geometrical position and identical sequence parameters to maximize labeling efficiency inside a selected vessel and minimize it outside the labeling focus. However, the acqui- sition of two corresponding label and control images for each selected artery can make the measurements in super-selective ASL time-consuming. Moreover, the measurements are performed separately for each artery of interest, increasing the risk for patient move- ment between the acquisitions. More time-efficient approaches were already proposed using pulsed ASL (PASL) or vessel-encoded pCASL (VEPCASL) to visualize individual perfusion territories or selected arteries. These are either based on dual-vessel labeling and subsequently combining the resulting images with a single control condition (PASL) or on calculating selective angiograms or flow territories by acquiring non-selective tag

and control images alongside multi-vessel labeled images (VEPCASL) [4, 11, 12]. Other methods employ a rotating control condition or use an approach to tag the anterior and posterior circulation simultaneously using two labeling slabs [13, 14]. One advantage of super-selective pCASL compared to other approaches is the flexibility of positioning the labeling spot. For example, the arteries of interest do not need to have a sufficiently long straight segment in a single plane, but the labeling focus for each artery can be adapted to the different vessels individually, e.g. by tilting the labeling spot in arbitrary direc- tions according to the course of the artery [4, 7]. Additionally, the size of the focus can be manually adjusted and thereby optimized with respect to individual vessel diameters [7]. Especially in patients with an altered vasculature, this can become an advantage compared to methods that apply large labeling slabs or require positioning a single la- beling plane so that the blood in all arteries of interest will experience sufficient labeling. In some clinical applications, information about selected arteries only, rather than the whole vasculature, can be of interest. Furthermore, patients most likely already under- went non-selective angiography, e.g. time-of-flight (TOF) imaging. Usually, this makes it possible to gather a lot of information about a pathology. However, acquiring images of only the relevant arteries (e.g. just the left carotid and left vertebral artery) may be sufficient for a proper diagnosis. In multi-vessel tagging approaches, it is often required to perform many encoding cycles with respect to the number of supplying arteries in order to decode single arterial flow territories [4]. The aim of this work is to present a method of time-resolved artery-selective ASL angiography imaging that is based on super-selective pCASL, denoted as cycled super-selective pCASL as this approach al- lows for flexible positioning of the labeling spot on top of individual arteries, which demonstrated to be beneficial in patients with an altered vasculature [15]. To reduce scan time compared to the conventional super-selective approach, a single non-selective control condition is used [4, 11, 13]. However, compared to conventional super-selective ASL, using a non-matching control acquisition might increase the deviation between control and label image, leading to higher labeling efficiencies in non-selected vessels. To assess the feasibility of cycled super-selective ASL, numerical simulations of the la- beling efficiency in- and outside a selected artery of interest are performed and validated in a volunteer study. Additionally, SNR measurements of the resulting angiograms are compared to conventional super-selective ASL and non-selective ASL, the latter being considered the gold-standard method of acquiring ASL angiograms in this study.

Figure 4.1:_{a) Planning of the individual acquisitions to obtain artery selective images.} The right internal carotid artery (RICA) is tagged in acquisition one (red), the left internal carotid artery (LICA) in acquisition two (green) and both vertebral arteries (VAs) in acquisition three (blue). In the latter case, the gradient moments of the super- selective labeling gradients are adjusted to form an ellipsoid labeling focus [14]. The fourth acquisition (control condition) is performed without transversal gradients. b) Representative locations for placing regions of interests (ROIs) for the SNR analysis.

Methods

Numerical Simulations and validation

To assess the feasibility of the proposed method, numerical simulations of the labeling efficiency were performed. These were conducted by numerically solving the Bloch equations over blood flow velocities of 1-60 cm/s, assuming laminar flow and a vessel diameter of 3.5 mm [7]. The simulation process assumed fully relaxed spins at the beginning of the experiment and took into account the inversion profile of the Hanning shaped RF pulses used during pCASL tagging. In-between two pulses, the phase change due to the perpendicular applied gradients was calculated. For each calculated velocity, this was repeated until the spins passed the simulated inversion slab. Used pCASL parameter were a flip angle of 18 with a pulse spacing of 1ms. The mean tagging gradient was set to 0.6 mT/m and the maximum gradient amplitude to 6 mT/m. As relaxation parameter, T1 and T2 of blood were assumed to be 1650ms and 100ms respectively. For cycled super-selective ASL, super-selectivity was achieved using a gradient moment of 1.08 mT/m in anterior-posterior and right-left direction during the pCASL pulse train for the labeling condition, and with zero gradient moment in the control condition. For conventional super-selective ASL the extra gradients were performed in both the label and control experiment [7]. To visualize the deviations in labeling efficiency over the

Figure 4.2: _{Schematic sequence diagram for the acquisitions 1-3 and acquisition 4} in the presented methods. The length of the labeling/control pulse train is displayed only partially. After presaturation of the static tissue, in acquisitions 1-3, the extra gradients for the selective labeling method are applied, achieving tagging of a single artery. In the fourth acquisition (control condition), no extra gradients are applied and the phase of every second RF pulse is shifted by 180 to achieve zero net inversion.

distance to the tagged artery, the calculations were performed over a range of 060 mm. The simulations were then validated by acquiring images of 5 volunteers while shifting the labeling focus over the same distance in 5 mm increments away from the selected artery for both the super-selective and cycled super-selective approach using a labeling duration of 1000ms without labeling delay. Image readout was performed using a 3D fast spoiled gradient echo sequence with a spatial resolution of 0.9x0.9mm2_{and 20 slices}

with a thickness of 3 mm each. Additionally, a non-selective scan was performed using the same scan parameters. The dependence of the labeling efficiency on the distance to the tagged artery was measured directly above the labeling plane using region of interest (ROI) analysis inside the arteries in the subtracted images of the cycled and the super- selective approach. The results were normalized with respect to the signal measured in the non-selective acquisition (Eq. 4.1). The labeling efficiency thus was calculated as

Labelingef f iciency =(SIcontrol− SIlabel)conventional/cycled (SIcontrol− SIlabel)non−selective

(4.1)

Calculation of the subtraction angiograms

In non-selective and super-selective ASL, two images are acquired. The first (Label) is subtracted from the second (Control) to obtain the final artery-selective angiogram. The acquisitions of one pair of label and control images form one cycle. This has to be repeated for each artery in the case of conventional super-selective ASL. In the presented approach of cycled super-selective ASL, one cycle is split into four individual acquisitions during the scan. One label acquisition is performed for each of the major arteries and a non-selective control condition is acquired for subsequent subtraction. In this method, the four acquisitions form one cycle. In this study, the left and right carotid arteries (LICA, RICA) and the vertebral arteries (VAs) were tagged selectively. Subtraction of acquisition four (Control) with each of the label acquisitions then results in the final angiographic images.

MR Experiments

The data acquired for this study was part of a general protocol for MRI pulse-sequence development approved by the local ethical committee. All volunteers gave written in- formed consent before they underwent MR imaging. The study population included 13 healthy volunteers (8 women and 5 men, mean age 28.2 years) without any known history of vascular disease. All imaging experiments were performed on a Philips 3T Achieva MR scanner (Philips Healthcare, Best, The Netherlands) using a 32 channel receive head coil. To plan the selective ASL measurements, the information about the position of the arteries is obtained via a TOF angiography scan placed over the neck [9]. An ex- ample of the geometry used to tag the major arteries is given in figure 4.1. A schematic overview of the pulse sequence is shown in figure 4.2. In order to visualize the leading edge of the labeled blood bolus, the labeling duration was set to 400ms and a 50ms post labeling delay was used. WET presaturation pulses were applied prior to labeling to reduce static tissue signal. For selective labeling of the ICAs, the additional gradient moments were adjusted to 1.08 mT/m in the anterior-posterior (Gx) and left-right (Gy) direction, for the VAs the zeroth moment of the gradient in left-right direction was set to 0.1 mT/m to achieve an ellipsoid labeling focus, thus make it possible to label both VAs at the same time (Fig. 4.1a) [7, 15, 16]. Image acquisition was performed using a 3D fast spoiled gradient echo readout sequence and a flip angle of 10◦_{. Each repetition}

of the label/control loop acquired 16 lines in k-space. Acquisition of the selective label images and the control image is performed in an interleaved way, meaning that after each partial k-space readout of one artery, a different labeled artery (or the control image) is acquired. This has the advantage of being more robust in terms of small continuous

head movement of the patients head, as the acquisitions are performed closer together in time [12]. The TR/TE was set to 7.9/3.7 ms and the acquisition matrix used was 240x240 with 0.9 mm3_{isotropic voxel size. 120 slices were acquired with a SENSE fac-}

tor of three, covering a volume of 10.8cm that displays the entire vasculature including and above the circle of Willis. Six consecutive time points after labeling were acquired with a temporal resolution of 150 ms in order to image the inflow of labeled blood. No cardiac triggering was performed. The total scan durations for the different approaches are summarized in table 1. In one volunteer, the cycled super-selective approach was additionally performed above the CoW, namely in the anterior (ACA) and both middle cerebral arteries (MCAs) using the same ASL and readout parameter as described above and compared to conventional super-selective ASL. The number of slices was halved to 60 and the stack placed directly above the super-selective tagging positions. All images were exported and post-processed using Matlab R2013b (The Mathworks, Natick, MA). This included image subtraction and the generation of maximum intensity projections (MIPs). To obtain a holistic picture of the cerebral vasculature, the selective MIPs were also merged into a single color-encoded frame.

SNR measurements

To compare the SNR of the selective methods, the non-selective ASL angiography scan was considered the reference method, as the labeling efficiency can be expected to be more consistent across the volunteers [7]. To perform quantitative comparisons, the mean SNRs were calculated by performing ROI analysis using ImageJ (ImageJ, Bethesda, MD) and calculated using the formula

SN Rmean=Smean

σBG

(4.2)

where Smeanis the mean signal of the ROI covering the arteries and σBGis the standard

deviation of the background signal [17]. The measurements were performed at 7 defined locations in each of the major arteries for all acquired time points. These are presented schematically on a transversal MIP in figure 4.1b. The ROI was then copied to the other images of the same volunteer to avoid any misplacing or contouring bias. Statistical analysis was performed using ANOVA testing to assess differences between the three methods [18]. A p-value of 0.05 was considered as statistically significant. Additionally, the Pearsons correlation coefficient was calculated for all major arteries for the three methods. To assess similarity between the approaches, Bland-Altman plots were created considering non-selective ASL as the reference method [19].

Figure 4.3: _{a) Simulated and measured labeling efficiencies as a function of distance} to the selected artery for conventional and cycled super-selective ASL. Error bars of the measured data represent the standard deviation from acquisitions in five volunteers. Note that the result from the cycled method is shifted to the right at distance zero for better visualization. b) Simulation of the labeling efficiency of cycled super-selective ASL in conjunction with the longitudinal magnetization values as a function of the distance to the labeled artery. Shown are the magnetic state of the control image (dashed line), of the label image (dotted line) and the resulting labeling efficiency as

Table 4.1: _{Total scan durations to acquire images of the three major arteries for the} different tagging methods.

Labeling Type Total duration (min.)

non-selective ASL 5:08

super-selective ASL 15:24 (5:08 per artery)

Cycled super-selective ASL 10:17

Results

Numerical Simulations and validation

The results from the numerical simulations and the corresponding volunteer measure- ments are shown in figure 4.3. The simulated and measured labeling efficiencies for the super-selective approach are in concordance with the values presented in the original publication [7]. The results from the simulation of the cycled super-selective method in- dicate a higher labeling efficiency (oscillating between +20 % and +40 %) as a function of the distance to the tagged artery, compared to conventional super-selective ASL hav- ing an oscillating efficiency between -20 % and +20 %. The efficiency of both methods within approximately 3 mm from the labeling focus appears identical.

MR Experiments

Image acquisition was performed successfully and all datasets could be used for the SNR measurements. Figure 4.4 shows representative MIPs of the final images of one volunteer after combining all vessels into a single color-encoded frame. An example of tagging smaller intracranial arteries above the circle of Willis is given in figure 4.5. Compared to conventional super-selective ASL (Fig. 4.5a), the cycled approach shows signal from the ACA when tagging the left MCA and vice versa (fig. 4.5b). This was not observed on the right MCA.

SNR measurements

The SNR of non-selective, conventional super-selective and cycled super-selective ASL imaging compare similarly, which can be seen in the Bland-Altman plots (Fig. 4.6). Following statistical analysis, no trend between the tagged arteries could be observed. The ANOVA analysis yielded a p-Value of 0.51 for the RICA, 0.46 for the LICA and 0.55 for the posterior circulation indicating no statistically significant differences between the

Figure 4.4: _{Representative transversal maximum intensity projections of one volun-} teer for the three performed methods. Only the first, third and fifth time frame (50, 350 and 650ms) are shown. The non-selective angiography presents all arteries during a single scan. The individually tagged major arteries in case of selective acquisitions are color-encoded, whereas the right ICA is displayed red, the left ICA green and the

posterior circulation blue.

methods in terms of SNR. The calculated Pearsons correlation coefficients also show a high correlation ( 0.78) between the methods and can be found in table 2.

Discussion

In this study an approach for the accelerated visualization of individual arteries in the cerebral vasculature, denoted as cycled super-selective pCASL is proposed and evaluated in terms of SNR compared to the conventional super-selective and non-selective pCASL angiography approach. Generally, to obtain angiographic images using ASL, a label

Figure 4.5: _{Example of super-selective tagging above the circle of Willis using con-} ventional super-selective (a) and cycled super-selective ASL (b). In (a), no unwanted signal can be seen, while in (b) the left middle cerebral artery (MCA) is visible when the anterior cerebral artery (ACA) was tagged and vice versa. This could be explained using the simulations of the labeling efficiency presented in figure 4.3, as the right MCA had a distance of 16 mm (minimum) and the left of 21.4 mm (maximum) to the ACA. Table 4.2: _{Pearsons correlation coefficients of the SNR measurements visualized as} correlation table for the three methods in the three major arteries that have been

visualized.

Non-selective Conventional Cycled

Visualized artery

RICA LICA VA RICA LICA VA RICA LICA VA

Non-selective 1 1 1

Conventional 0.82 0.87 0.79 1 1 1

Cycled 0.82 0.81 0.88 0.84 0.87 0.78 1 1 1

image has to be performed with a matching control image. These should have the same contrast properties, except for the magnetic state of the inflowing labeled blood in order to subtract the static background tissue as accurately as possible. In super-selective ASL, the additionally performed gradients perpendicular to the labeling plane could cause adverse effects, so that it is important to acquire a corresponding control condition, in which the same gradient pattern is used [7]. Influencing factors of the labeling efficiency are the gradient scheme, the geometrical position as well as the strength of the additional gradients defining the size and shape of the labeling focus [7, 16].

Figure 4.6: _{Bland-Altman plots for the three evaluated methods with non-selective}